[Day 215] Trying out 'traditional' models on the KB project transaction fraud data
Hello :) Today is Day 215! A quick summary of today: trying out different models to classify non fraud/fraud transactions In addition to the Graph Convolutional Network model, I wanted to create some 'traditional' (non-neural net) models for better comparison and judgement. The features I used for the below models are: numerical: amt (amount) categorical: category, merchant, city, state, job, trans_hour, trans_dow Then I downsampled the majority class (non fraud cases; just like for the GCN model). Logistic Regression Best params: {'C': 1, 'penalty': 'l1', 'solver': 'liblinear'} CatBoost Best params: {'depth': 6, 'iterations': 300, 'learning_rate': 0.3} XGBoost Best params: {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 300, 'subsample': 0.9} RandomForest Best params: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 200} LGBMClassifier Bes